Convert multiple boolean columns which names start with string `abc_` at once into integer dtype

Question

I need to have 1 and 0 instead of True and False in a pandas data frame for only columns starting with abc_. Is there any better way of doing this other than my loop:

for col in df:
  if col[:4] =='abc_':
     df[col] = df[col].astype(int) 

Answer 1

Option 1: converting all boolean (dtype == 'bool') columns

df.loc[:, df.dtypes.eq('bool')] = df.loc[:, df.dtypes.eq('bool')].astype(np.int8)

Option 2: if only those boolean columns that start with abc_ should be converted:

col_mask = df.dtypes.eq('bool') & df.columns.str.contains('^abc_')
df.loc[:, col_mask] = df.loc[:, col_mask].astype(np.int8)

Option 3: converting only by column names

df.loc[:, df.columns.str.match(r'^abc_.*$')] = \
    df.filter(regex=r'^abc_').astype(np.int8)

Answer 2

You can do this with filter and an in-place update.

df.update(df.filter(regex='^abc_').astype(int))